EEMD Empirical Mode Decomposition Combined with ANN Neural Network Classification

In this article, we explore an innovative data processing methodology integrating Ensemble Empirical Mode Decomposition (EEMD) with Artificial Neural Network (ANN) classification. EEMD serves as an adaptive time-frequency analysis technique that decomposes nonlinear signals into intrinsic mode functions (IMFs), while ANN provides a powerful nonlinear modeling framework for pattern recognition. The implementation typically involves applying EEMD to decompose raw signals into stable IMF components, followed by extracting meaningful features from these IMFs to train ANN classifiers. Key computational steps include: 1) EEMD decomposition with multiple noise-assisted realizations to overcome mode mixing, 2) Feature engineering from IMF statistics (e.g., energy entropy, instantaneous frequency), and 3) ANN architecture design using multilayer perceptrons with backpropagation optimization. This hybrid approach enables robust analysis of complex datasets by leveraging EEMD's noise-resistant decomposition and ANN's superior classification capabilities, ultimately yielding more accurate and interpretable results in practical applications such as biomedical signal processing and mechanical fault diagnosis.